Instrumented Principal Component Analysis

Kelly, Bryan T.; Pruitt, Seth; Su, Yinan

doi:10.2139/ssrn.2983919

Cited by 41 publications

(40 citation statements)

References 23 publications

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“…This lack of identification means that we cannot estimate a generic time-varying unobservable structure from the spectral properties of a covariance matrix alone. A recent proposal in the direction of a functional specification for a time-varying θ i,t is the Instrumented Principal Components Analysis (IPCA) of Kelly et al (2017, which we review in Section 4 together with other inference approaches for latent factor models with time-varying betas. IPCA works with linear loading specifications, with balanced panels, and without observable factors.…”

Section: Model Diagnosticmentioning

confidence: 99%

“…Among the parametric approaches, Kelly et al (2017 model the coefficients as linear functions of characteristics plus some noise term:…”

Section: Inference In Models With Unobservable Factorsmentioning

confidence: 99%

“…Component Analysis (IPCA) estimator of Kelly et al (2017 is obtained by minimizing a LS criterion w.r.t. parameter matrices A, B and the factor values f t , t = 1, ..., T , i.e., min…”

Section: Inference In Models With Unobservable Factorsmentioning

confidence: 99%

“…Kelly et al (2017, andGu et al (2019). The framework allows for time-variation in the number of conditional factors as an effect of the changing macroeconomic environment.…”

mentioning

confidence: 99%

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Estimation of Large Dimensional Conditional Factor Models in Finance

2019

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This chapter provides an econometric methodology for inference in large-dimensional conditional factor models in finance. Changes in the business cycle and asset characteristics induce time variation in factor loadings and risk premia to be accounted for. The growing trend in the use of disaggregated data for individual securities motivates our focus on methodologies for a large number of assets. The beginning of the chapter outlines the concept of approximate factor structure in the presence of conditional information, and develops an arbitrage pricing theory for large-dimensional factor models in this framework. Then we distinguish between two different cases for inference depending on whether factors are observable or not. We focus on diagnosing model specification, estimating conditional risk premia, and testing asset pricing restrictions under increasing cross-sectional and time series dimensions. At the end of the chapter, we review some of the empirical findings and contrast analysis based on individual stocks and standard sets of portfolios. We also discuss the impact on computing time-varying cost of equity for a firm, and summarize differences between results for developed and emerging markets in an international setting.

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Section: Model Diagnosticmentioning

confidence: 99%

“…Among the parametric approaches, Kelly et al (2017 model the coefficients as linear functions of characteristics plus some noise term:…”

Section: Inference In Models With Unobservable Factorsmentioning

confidence: 99%

“…Component Analysis (IPCA) estimator of Kelly et al (2017 is obtained by minimizing a LS criterion w.r.t. parameter matrices A, B and the factor values f t , t = 1, ..., T , i.e., min…”

Section: Inference In Models With Unobservable Factorsmentioning

confidence: 99%

“…Kelly et al (2017, andGu et al (2019). The framework allows for time-variation in the number of conditional factors as an effect of the changing macroeconomic environment.…”

mentioning

confidence: 99%

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Estimation of Large Dimensional Conditional Factor Models in Finance

2019

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“…As before, we construct forecast intervals that are robust to the strength of γ lt , and is uniformly valid over a large class of DGPs that allows different types of time-variations in γ lt . A similar decomposition to (1.1) was given by Kelly et al (2017), where beta is decomposed into a linear function of lagged instruments as well as a unobservable loading component. They specifically require γ t be strong, and obtained limiting distributions for the "instrumental betas", which are therefore, not uniformly valid.…”

Section: Beta Decompositionsmentioning

confidence: 99%

Uniform Inference for Conditional Factor Models with Instrumental and Idiosyncratic Betas

Liao

Yang

2017

SSRN Journal

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It has been well known in financial economics that factor betas depend on observed instruments such as firm specific characteristics and macroeconomic variables, and a key object of interest is the effect of instruments on the factor betas. One of the key features of our model is that we specify the factor betas as functions of time-varying observed instruments that pick up long-run beta fluctuations, plus an orthogonal idiosyncratic component that captures high-frequency movements in beta. It is often the case that researchers do not know whether or not the idiosyncratic beta exists, or its strengths, and thus uniformity is essential for inferences. It is found that the limiting distribution of the estimated instrument effect has a discontinuity when the strength of the idiosyncratic beta is near zero, which makes usual inferences fail to be valid and produce misleading results. In addition, the usual "plug-in" method using the estimated asymptotic variance is only valid pointwise. The central goal is to make inference about the effect on the betas of firms' instruments, and to conduct outof-sample forecast of integrated volatilities using estimated factors. Both procedures should be valid uniformly over a broad class of data generating processes for idiosyncratic betas with various signal strengths and degrees of time-variant. We show that a cross-sectional bootstrap procedure is essential for the uniform inference, and our procedure also features a bias correction for the effect of estimating unknown factors.

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